56
reads

Ruby C. Weng

2012-10-19
12:30:00 - 14:30:00

103 , Mathematics Research Center Building (ori. New Math. Bldg.)

For Internet games, large online ranking systems are much needed. We propose a Bayesian approximation method, based on a variant of Stein's identity (1981),to obtain online ranking algorithms with simple analytic update rules. Experiments on game data show that the accuracy of our approach is competitive with state of the art systems such as TrueSkill, but the running time as well as the code are much shorter. We also compare our method with Glicko rating system, which is designed for rating chess players.